|About this Abstract
||2022 TMS Annual Meeting & Exhibition
||AI/Data Informatics: Computational Model Development, Validation, and Uncertainty Quantification
||Combined Clustering and Regression for Predicting Melting Temperatures of Solids
||Vahe Gharakhanyan, Josť Antonio Garrido Torres, Ethan Eisenberg, Snigdhansu Chatterjee, Dallas Trinkle, Alexander Urban
|On-Site Speaker (Planned)
Melting temperature is important for materials design because it determines the temperature stability of solids. The use of empirical and computational melting point estimation techniques is limited by scope and computational feasibility, respectively. Machine learning (ML) has previously been used for predicting melting temperatures for a small number of binary compounds and certain material classes. Using a database of melting points of 600 crystalline binary compounds and compound features constructed from elemental properties and zero-Kelvin DFT calculations as model input, we first evaluated a direct supervised-learning strategy for melting temperature prediction. We find that the fidelity of predictions can further be improved by introducing an additional unsupervised-learning step that first classifies the materials before melting-point regression. Not only does this two-step model exhibit an improved accuracy but the approach also provides additional insights into different types of melting that are dependent on the unique atomic interactions inside a material.
||Machine Learning, Computational Materials Science & Engineering, Phase Transformations